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The great potential of teleradiology is now recognised. Today, it has earned a reputation as a professional service that supports medical institutions in their daily tasks. And now, Artificial Intelligence (AI) is the next innovation in medical diagnostics.

Teleradiology already has a significant influence on the field of radiology. It contributes to significantly improving the quality of medicine through its high-quality diagnostic sub-specialisation that can be used at any desired location. Teleradiology also helps to make existing radiological workplaces in hospitals and specialist radiological institutes more attractive in the long term and even to prevent burnout: obligatory services at unloved times, such as at night, at weekends and on public holidays, are simply no longer necessary for radiologists from such institutions. And in the event of an excessively high work volume, for example during peak or off-peak times or in the event of staff shortages, we at ERS – Emergency Radiology Schueller – can take over and report on any number of examinations for our customers at any time in the shortest possible time and with our high quality.

But that is not all. We at ERS – Emergency Radiology Schueller – are constantly questioning ourselves and are already thinking today about those services which will make their contribution to serve society in the best possible way in the future. Revolutionary information technology has enabled the emergence of both teleradiology and AI. The rapid development of telecommunications has helped teleradiology to celebrate its great success. The development of the DICOM standard, which enabled the storage and exchange of information in medical image data management, had a particular influence on this.  For AI, the development of algorithms of neural networks in connection with the latest generation of powerful processors offers the possibility of processing large amounts of data in parallel.

What is the benefit of a synergy between these two services? Read more in part 2!

The cumulative effect of the exposure to CT imaging compounds the need to achieve the lowest per-scan patient dose possible. While low-dose CT imaging techniques exist, there is often a compromise that must be made in both image quality and cost. Lower-dose studies have higher image noise, which results in lower quality images, which are more difficult to analyze. Iterative Reconstruction (IR) is still the most used technique for improving the quality of lower-dose imaging studies. However, IR can take substantially longer to process images and is prone to producing images with a waxy or blurry appearance if applied too strongly on low-dose studies. Deep Learning CT Processing (DLCP) is the next generation in CT image noise reduction techniques. DLCP not only improves CT images acquired during typically low-dose procedures like lung screening and pediatric exams, but it also presents the ability to significantly reduce radiation exposure for other higher-risk categories such as oncology and obese patients. In addition, it can improve the image quality of ultra-low-dose abdominal, cardiac, and brain scans. Radailogy presents PixelShine by AlgoMedica in order to assist to the reduction of CT radiation dose and from now on to substantially improve image quality in low dose CT studies.